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Structural Modelling of Dynamic Networks and Identifying Maximum Likelihood

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  • Christian Gourieroux
  • Joann Jasiak

Abstract

This paper considers nonlinear dynamic models where the main parameter of interest is a nonnegative matrix characterizing the network (contagion) effects. This network matrix is usually constrained either by assuming a limited number of nonzero elements (sparsity), or by considering a reduced rank approach for nonnegative matrix factorization (NMF). We follow the latter approach and develop a new probabilistic NMF method. We introduce a new Identifying Maximum Likelihood (IML) method for consistent estimation of the identified set of admissible NMF's and derive its asymptotic distribution. Moreover, we propose a maximum likelihood estimator of the parameter matrix for a given non-negative rank, derive its asymptotic distribution and the associated efficiency bound.

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  • Christian Gourieroux & Joann Jasiak, 2022. "Structural Modelling of Dynamic Networks and Identifying Maximum Likelihood," Papers 2211.11876, arXiv.org.
    • Handle: RePEc:arx:papers:2211.11876
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    References listed on IDEAS

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